{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用pandass打开xlsx文件，并命名为pd1 \n",
    "import pandas as pd\n",
    "df1 = pd.read_excel(r'E:\\project\\sim_auto\\src\\test.xlsx',sheet_name=\"Sheet2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T1</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T2</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T3</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T4</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T5</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>T6</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T7</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T8</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>T9</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>T10</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>T11</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>T12</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>T13</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>T14</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>T15</td>\n",
       "      <td>79441.666667</td>\n",
       "      <td>161125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A             B       C\n",
       "0    T1  79441.666667  161125\n",
       "1    T2  79441.666667  161125\n",
       "2    T3  79441.666667  161125\n",
       "3    T4  79441.666667  161125\n",
       "4    T5  79441.666667  161125\n",
       "5    T6  79441.666667  161125\n",
       "6    T7  79441.666667  161125\n",
       "7    T8  79441.666667  161125\n",
       "8    T9  79441.666667  161125\n",
       "9   T10  79441.666667  161125\n",
       "10  T11  79441.666667  161125\n",
       "11  T12  79441.666667  161125\n",
       "12  T13  79441.666667  161125\n",
       "13  T14  79441.666667  161125\n",
       "14  T15  79441.666667  161125"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用matplotlib库根据dataframe数据画折线图\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "\n",
    "# 读取数据\n",
    "df = df1\n",
    "\n",
    "# 画折线图\n",
    "plt.plot(df['A'], df['B'])\n",
    "plt.plot(df['A'], df['C'])\n",
    "plt.xlabel('zxc')\n",
    "plt.ylabel('C') \n",
    "plt.title('Line Chart') \n",
    "# 显示图形\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
